Determination of Moisture Content in Wheat Using Competitive Adaptive Reweighted Sampling and Two-Dimensional Convolutional Neural Networks
A near infrared spectroscopy(NIRS)system was utilized to obtain wheat spectra,and then a two-dimensional convolutional neural network model(CARS-GADF-2DCNN)based on competitive adaptive reweighted sampling(CARS)and gramian angular difference field(GADF)was developed in this study.The CARS-GADF-2DCNN model employed CARS to identify characteristic wavelengths from the NIR spectra,converted the NIR spectra into two-dimensional images using GADF,and finally employed the 2DCNN to learn image features for quantitative analysis of wheat moisture content.The predictive performance of this model was evaluated in comparison with other models.The results demonstrated that CARS-GADF-2DCNN model improved the prediction accuracy for wheat moisture content by 68.8%,45.6%,20.2%,and 17.5%compared to 1DCNN,GADF-2DCNN,VCPA-GADF-2DCNN,and IRIV-GADF-2DCNN,respectively.In summary,the CARS-GADF-2DCNN alleviated the issues of low prediction accuracy and overfitting in NIRS modeling.This study provides an accurate and rapid method for determining wheat moisture content.
万方数据
维普
